{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "173939c0",
   "metadata": {},
   "source": [
    "### P079 异常值检测-LOF算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2596b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import LocalOutlierFactor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4f68f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab89cc06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./p079.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a142d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "757a82db",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.values\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69ed8eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "lof = LocalOutlierFactor(n_neighbors=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02202b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lof.fit_predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "608d74ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25f4b913",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"lof\"] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e3716d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04bdc53",
   "metadata": {},
   "source": [
    "### P080 异常值检测-LOF统计离群点个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2a7db75",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "466de25e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"lof\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "012da17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query(\"lof==-1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "728c397f",
   "metadata": {},
   "source": [
    "### P081 异常值检测 - 孤立森林Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a79a579e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import IsolationForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed2c4095",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "df = pd.read_csv(\"./p081.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07dd814e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_length</th>\n",
       "      <th>item_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.206380</td>\n",
       "      <td>5.618286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.756649</td>\n",
       "      <td>5.921924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.576329</td>\n",
       "      <td>5.792401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.304950</td>\n",
       "      <td>7.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.764064</td>\n",
       "      <td>5.965792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_length  item_width\n",
       "0     6.206380    5.618286\n",
       "1     5.756649    5.921924\n",
       "2     5.576329    5.792401\n",
       "3     5.304950    7.235000\n",
       "4     6.764064    5.965792"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d8cc86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier = IsolationForest(\n",
    "    n_estimators=100,\n",
    "    contamination=0.05,\n",
    "    random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5672091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsolationForest(contamination=0.05, random_state=42)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0bf84c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"outlier_flag\"] = outlier.predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1106d94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_length</th>\n",
       "      <th>item_width</th>\n",
       "      <th>outlier_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.206380</td>\n",
       "      <td>5.618286</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.756649</td>\n",
       "      <td>5.921924</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.576329</td>\n",
       "      <td>5.792401</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.304950</td>\n",
       "      <td>7.235000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.764064</td>\n",
       "      <td>5.965792</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>4.417224</td>\n",
       "      <td>2.476044</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>3.208315</td>\n",
       "      <td>3.746246</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1.252213</td>\n",
       "      <td>3.083772</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.463473</td>\n",
       "      <td>3.951189</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>3.413449</td>\n",
       "      <td>0.592448</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     item_length  item_width  outlier_flag\n",
       "0       6.206380    5.618286             1\n",
       "1       5.756649    5.921924             1\n",
       "2       5.576329    5.792401             1\n",
       "3       5.304950    7.235000             1\n",
       "4       6.764064    5.965792             1\n",
       "..           ...         ...           ...\n",
       "395     4.417224    2.476044            -1\n",
       "396     3.208315    3.746246            -1\n",
       "397     1.252213    3.083772            -1\n",
       "398     0.463473    3.951189            -1\n",
       "399     3.413449    0.592448            -1\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64bf0c4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a712669c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee2f416e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d92f9ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdf933fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dcbaca8d",
   "metadata": {},
   "source": [
    "### P082 异常值检测 - 孤立森林可视化异常点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac9bc18b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_length</th>\n",
       "      <th>item_width</th>\n",
       "      <th>outlier_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.206380</td>\n",
       "      <td>5.618286</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.756649</td>\n",
       "      <td>5.921924</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.576329</td>\n",
       "      <td>5.792401</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.304950</td>\n",
       "      <td>7.235000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.764064</td>\n",
       "      <td>5.965792</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_length  item_width  outlier_flag\n",
       "0     6.206380    5.618286             1\n",
       "1     5.756649    5.921924             1\n",
       "2     5.576329    5.792401             1\n",
       "3     5.304950    7.235000             1\n",
       "4     6.764064    5.965792             1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec601fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    380\n",
       "-1     20\n",
       "Name: outlier_flag, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"outlier_flag\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca1ea40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3266adda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='item_width', ylabel='item_length'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(\n",
    "    x = df[\"item_width\"],\n",
    "    y = df[\"item_length\"],\n",
    "    hue= df[\"outlier_flag\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22b2280c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c76fd44f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beddc773",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32936b28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
